Acoustic Emission Monitoring of Progressive Damage of Reinforced Concrete T-Beams under Four-Point Bending

Abstract

Acoustic Emission (AE) is revealed to be highly adapted to monitor materials and structures in materials research and for site monitoring. AE-features can be either analyzed by means of physical considerations (geophysics/seismology) or through their time/frequency waveform characteristics. However, the multitude of definitions related to the different parameters as well as the processing methods makes it necessary to develop a comparative analysis in the case of a heterogeneous material such as civil engineering concrete. This paper aimed to study the micro-cracking behavior of steel fiber-reinforced reinforced concrete T-beams subjected to mechanical tests. For this purpose, four-points bending tests, carried out at different displacement velocities, were performed in the presence of an acoustic emission sensors network. Besides, a comparison between the sensitivity to damage of three definitions corresponding to the b-value parameter was performed and completed by the evolution of the RA-value and average frequency (AF) as a function of loading time. This work also discussed the use of the support-vector machine (SVM) approach to define different damage zones in the load-displacement curve. This work shows the limits of this approach and proposes the use of an unsupervised learning approach to cluster AE data according to physical and time/frequency parameters. The paper ends with a conclusion on the advantages and limitations of the different methods and parameters used in connection with the micro/macro tensile and shear mechanisms involved in concrete cracking for the purpose of in situ monitoring of concrete structures.

Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission and Machine Learning

“…Unsupervised cluster analysis is a data-driven method that categorizes data points into clusters by grouping data points that possess similar characteristics or features. The AE signals are categorized by the similarity in their characteristics [39][40][41]. This study investigated the feature-based-counts, energy, absolute energy, rise time, initiation frequency, peak frequency, frequency centroid, amplitude, and duration selected to represent each AE signal-damage detection and classification methods using machine learning methods for monitoring BFRP-reinforced slabs with a reduced number of AE sensors to minimize the SHM cost.…”

The Challenges of long‐time high‐sampled Structural Health Monitoring: a practical Feedback

“…Acoustic sensing is a specific case of vibration measurement, usually separated from classical accelerometers because of the high frequency range involved, and because its aim is different: detection of defects like prestress wire breaking or crack opening in concrete [12].…”

Acoustic Emission Monitoring for Damage Assessment of a Magnetite Ultra-High-Performance Concrete (MUHPC) Block in a Bending Test

“…The parameterbased analysis method is currently being widely used in many applications to monitor the structural health of buildings due to its straightforward and uncomplicated nature [45]. Given that a single AE parameter may not provide a reliable assessment of damage to materials, structures, or crack mechanisms, it is necessary to employ complete AE parameter analysis methodologies, such as the b-value [46,47], the improved b-value (Ib-value) [48], RA-AF association analysis method [49], and AE intensity analysis method, which are extensively utilized for monitoring concrete structure damage using AE technology [50].…”